Editor
Yair Cohen
Reviewed by
Yair Cohen
Yair brings a wealth of experience in cybersecurity and data product management. In his previous role, Yair successfully doubled the revenue of the Datadog Infrastructure monitoring product, increasing it from $250 million ARR to $500 million ARR. With a background as a member of the IDF's Unit 8200 for five years, he possesses over 18 years of expertise in enterprise software, security, data, and cloud computing. Yair has held senior product management positions at Datadog, Digital Asset, and Microsoft Azure Protection.
Technical Reviewer
Ron Reiter
Reviewed by
Ron Reiter
Ron has more than 20 years of tech hands-on and leadership experience, focusing on cybersecurity, cloud, big data, and machine learning. Following his military experience, Ron built a company that was sold to Oracle. He became a serial entrepreneur and a seed investor in several cybersecurity startups, including Axonius, Firefly and Lightricks.
What is Data Security Posture Management (DSPM)?
Data Security Posture Management is an approach to securing cloud data by ensuring that sensitive data always has the correct security posture - regardless of where it’s been duplicated or moved to.
Here’s a quick example:
Let’s say you’ve built an excellent security posture for your cloud data. For the sake of this example, your data is in production, it’s protected behind a firewall, it’s not publicly accessible, and your IAM controls have limited access properly. Now along comes a developer and replicates that data into a lower environment.
What happens to that fine security posture you’ve built? Well, it’s gone - and now the data is only protected by the security posture in that lower environment. So if that environment is exposed or improperly secured - so is all that sensitive data you’ve been trying to protect.
Security postures just don’t travel with their data. Data Security Posture Management (DSPM) was created to solve this problem and minimize data risks.
Inside DSPM: How Does It Protect Sensitive Data?
If we want a data security posture that travels with the data and helps you remediate issues, we need a solution that does three things:
- Discovers all the data in your public cloud environment - including shadow data that’s been created but isn’t used or monitored.
- Understands what security posture the data is supposed to have
- Prioritizes alerts based on data sensitivity and offers contextualized remediation plans
Data discovery and classification tools have been around for years. But they’ve lacked the ability to offer any business context. If you can find sensitive data but don’t know whether it’s business critical or not, and don't understand its security posture, it’s not much help to the security team that’s trying to prioritize thousands of alerts from different tools.
For example, let’s say a data discovery tool finds PII data. You wouldn’t need an alert if it has the proper security posture. A good DSPM solution wouldn’t waste your time with one.
Read more about how DSPM works and its different use cases.
How Does DSPM Understand What Data is Sensitive?
Some data is obviously sensitive - social security numbers, credit card information, and healthcare data for example. These need to be protected not only for security reasons, but to stay compliant with regulations like PCI-DSS, HIPAA, and more.
But a good DSPM solution needs to go beyond this. To truly provide value, it should be able to autonomously draw conclusions about the type of sensitive data it’s finding - and be able to find data that isn’t structured as simply as a credit card number. By understanding and clustering metadata and leveraging ML technologies, DSPMs can find intellectual property, customer data and more that can’t be discovered just from using regular expressions.
Another critical factor is data ownership. DSPM should integrate with data catalogs to understand who is responsible for the data. Finally, there’s the issue of scale. One of the major weaknesses of legacy data discovery and classification solutions is that they aren’t able to scan and classify and the scale of modern cloud infrastructures. DSPM must be able to scan petabytes of data effectively and efficiently, to ensure everything is discovered - without breaking your cloud bill.
Key Elements of Data Security Posture Management
Key elements of DSPM can vary somewhat depending on an organization's unique use case, risk tolerance, regulatory requirements, and the nature of its sensitive data. Regardless of these factors, the following elements are universally relevant and help enterprises form the foundation of an effective DSPM strategy.
Why are Cloud-First Enterprises Adopting DSPM?
A cloud-first strategy prioritizes the adoption of cloud over legacy IT systems. Besides enabling enterprises to avoid the substantial costs associated with on-prem setups, this approach also offers scalable resources, high availability, and data redundancy. As a result, businesses can swiftly respond to changing market demands, rapidly scale their operations, and enhance overall resilience in the face of disruptions.
Before widespread adoption of public cloud infrastructure, securing sensitive data meant securing your data center with a firewall. Even if your data was copied or moved, it still stayed inside your organization’s data center. There wasn’t a difference between your infrastructure security and your data security. But for cloud-first companies, sensitive data travels constantly across your cloud, to environments with different security postures. So the need arose to build a product that makes sure all this traveling data has the right security posture.
While most of these organizations have implemented CSPM solutions to secure their cloud infrastructure, they’re now beginning to turn to DSPM to specifically target cloud data protection. DSPM’s cloud-first approach makes it easier for cloud-first companies to discover, classify, assess, prioritize, and remediate data security issues. By turning to solutions that automate data detection and protection, these enterprises are better able to address cloud data security concerns at a massive scale.
But that's not all – there are additional compelling factors that make the adoption of DSPM appealing to cloud-first enterprises.
Most cloud security tools were originally designed with a static, perimeter-focused approach. However, in dynamic cloud environments, where data is constantly moving and adapting, these cloud data security solutions fall short of offering all-in data protection.
Filling the gaps left by perimeter-focused methods, DSPM follows a dynamic approach to ensure that sensitive data retains its security posture, even when duplicated or moved. This essentially implies that enterprises are able to track and secure data throughout its lifecycle, across distributed cloud environments.
But can DSPM fit in the context of a broader security strategy?
Absolutely. By providing automatic visibility, risk assessment, and access analysis for cloud data, DSPM ensures that sensitive data is always secured. Specifically targeting the unique challenges of the cloud data landscape, DSPM not only complements traditional security practices but also elevates them by offering rich contextual information based on data sensitivity.
The Benefits of Implementing Data Security Posture Management
Advanced Risk Assessment and Threat Detection
DSPM solutions leverage advanced technologies like machine learning and AI to conduct real-time risk assessments and employ sophisticated threat detection algorithms. Unlike traditional practices, DSPM takes a proactive approach by analyzing data patterns and helping enterprises stay one step ahead of potential security incidents.
Improved Compliance and Data Governance
DSPM’s role in helping organizations achieve and maintain compliance with data protection regulations is one of the core benefits cloud-first enterprises look for. Data classification, data tagging, and automated policy enforcement are some of the core practices employed to enhance compliance and data governance. In addition, DSPM solutions also help enterprises enforce data security controls, monitor sensitive data access, generate compliance reports, and maintain audit trails for better regulatory compliance management.
Granular Access Controls and User Behavior Monitoring
DSPM empowers organizations with fine-grained access controls, allowing them to define precise permissions for secure data access. With advanced user behavior monitoring capabilities, DSPM can detect anomalous activities, monitor privileged user actions, and identify potential insider threats in real-time.
Efficient Incident Response and Remediation
DSPM solutions streamline incident response and remediation processes through real-time alerts, automated incident workflows & playbooks, and comprehensive audit logs. This ensures faster incident response times, mitigated impact of security incidents and effective remediation actions are taken in a timely fashion.
Seamless Integration With Cloud Service Providers (CSPs)
DSPM seamlessly integrates with most major CSPs and hyperscalers. This integration strengthens overall security by combining the specialized capabilities of DSPM with the native security measures offered by the cloud service provider.
What to Look for in a DSPM Solution?
Before choosing a DSPM solution for your enterprise, consider whether it offers the following:
Agentless Data Discovery
- Can your DSPM solution connect to your multi-cloud environment in minutes?
- Does it offer agentless data discovery that automatically and continuously discovers 100% of data stores without any impact on workload performance, and without the need to configure the connection to the data store or provide specific credentials?
Cloud-Native Data Classification
- Does your DSPM platform leverage machine learning and metadata clustering to automatically classify data with a high level of accuracy?
- Does your DSPM solution go beyond detecting PII and is able to detect and accurately label personal, financial, healthcare, and developer secrets, as well as proprietary data, including customer data, HR data, or intellectual property?
Security Posture Assessment
- Can your DSPM solution identify sensitive data assets with a weak security posture, including misconfigurations, encryption types, compliance violations, backups and logging for business continuity and auditing, and more?
- Can it define granular data security controls to secure sensitive data assets regardless of the infrastructure and applications the data is stored in?
Data Access Analysis
- Can your DSPM system understand at a glance who can and should take action on what data?
- Can it automatically raise timely alerts for security teams, such as when third party applications gain sudden access to sensitive data?
- Can it monitor IAM identities and roles and reduce the data attack surface by detecting dormant data, inactive users, unused access keys, users without MFA, and more – always with the context of the data at risk?
Data Movement Detection
- Does your DSPM platform of choice detect when data is copied across cloud data stores, including when it’s processed by data pipelines, ETLs, database migrations or backups?
- Can it define policies to alert when sensitive data is copied or moved between regions, environments, and networks?
- Is it able to gain rich context that outlines security drifts, such as excessive permissions, and bring together multiple data owners for easier remediation?
Integrations and Multi-Cloud Security
- Can your DSPM platform automatically route data security issues and compliance violations to the appropriate security teams via integrations to the tools they’re familiar with?
- And does it offer a multi-cloud data security platform that works across IaaS, PaaS and DBaaS, including AWS, Azure, GCP, Oracle Cloud, Snowflake and Databricks?
How to Implement Data Security Posture Management
Implementing DSPM requires a periodic iteration of different steps that help maintain a robust data security posture to counter evolving threats and misconfigurations.
Step 1: Discovery
In this initial phase, the objective is to map out all data assets across the organization. Much like an asset cataloging process, every single piece of data must be accounted for. This involves identifying data sources, cataloging databases, and understanding data flows across the organization.
This might sound straightforward, but given the dispersed nature of data in modern cloud environments, it's quite challenging. For instance, the discovery phase might involve different procedures for an organization that mainly handles structured data in databases versus an organization dealing with unstructured data spread across multiple cloud storage services.
This step also involves classifying data based on its sensitivity and importance to the organization. For improved efficiency and accuracy, this process can however be automated and enhanced with machine learning and AI technologies for identifying data sources, categorizing data, and mapping out data flows.
Step 2: Assessment
After cataloging the data, the next step is to evaluate its security posture and assess underlying vulnerabilities. As a result, this phase requires a deeper understanding of your data, administered security controls, specific compliance requirements, and your organization's security blueprint.
In particular, it's crucial that existing controls meet each data type's security requirements accurately, as well as identify any gaps or vulnerabilities proactively. Instead of being a one-off task, this is an ongoing process so that security mechanisms adapt to changing threat landscapes and organizational needs over time.
Step 3: Remediation
As part of the action phase, this step involves taking measures to address vulnerabilities and risks identified during the assessment phase. For instance, this may mean using encryption for sensitive data storage, restricting access to key databases or patching software vulnerabilities.
Success with remediation lies in having an efficient plan in place which uses both technology and human intervention to meet threats head on. While security tools may assist in detecting or blocking them, it remains the security team's role to deploy and configure them effectively and correctly.
Challenges of Implementing DSPM
Implementing DSPM, while crucial, comes with a set of challenges that can complicate the task. Some of these include the following:
The Data Sprawl Dilemma
In today's hyper-connected world, data sprawl poses significant challenges for implementing DSPM. With data distributed across numerous locations, a typical organization may deal with millions of data points that constantly shift across multiple nodes, services, and geographies.
Does data sprawl impact the effectiveness of security measures in the cloud environment?
It surely does:
- The root of the issue stems from the multiplicity of data repositories and the constant movement of data, both of which expand the potential attack surface and complicate visibility. This essentially implies that each data storage and processing location might require distinct security protocols, ultimately adding another layer of complexity.
- To manage data sprawl, enterprises often employ data mapping tools and federated data governance. However, the effectiveness of these strategies depends categorically on their ability to keep up with rapid data creation and movement. These, while effective, demand constant upkeep for data creation and migration, often requiring sophisticated CI/CD workflows and infrastructure-as-code practices.
- Limiting data sprawl involves encrypting data both at rest and in transit. However, implementing such encryption necessitates the careful management of cryptographic keys, further adding to the challenge. The need to ensure that these keys themselves are secure, requires a well-designed key management system.
- Achieving interoperability while maintaining operational fluidity between disparate systems—each with its own data format, API protocols, and security mechanisms—demand sophisticated solutions. Middleware can enhance communication and data management, while custom connectors ensure secure and efficient data transfer across platforms. In addition, comprehensive API management solutions offer better visibility and control over data interactions, fortifying data security. Although adopting these techniques is crucial to a secure and scalable cloud data environment, these are complex to implement.
Overcoming Lack of Data Awareness
Lack of data awareness, characterized by limited knowledge about its location, access patterns, and lifecycle trajectory can severely undermine security measures. Considering the mutable nature of data and the dynamic frequency of changes, this issue becomes even more daunting.
How does a lack of data awareness impede the effectiveness of a DSPM strategy?
- Tracing real-time data flow across microservices, maintaining API call logs, and ensuring comprehensive metadata documentation represents a non-trivial problem. Further complexities arise from encrypted data streams and data masking techniques used for privacy purposes, which add another layer of obfuscation.
- Although machine learning algorithms can provide predictive data behavior analysis, it's crucial to note the complexity of training these models to accurately capture data patterns in highly distributed systems.
- Considering data sensitivity levels, regulatory requirements, and operational needs, the implementation of data classification and tagging protocols require advanced execution capabilities.
Shadow IT - The Unsanctioned Threat
Shadow IT refers to the unauthorized use of IT systems and services by individuals or departments, where the principal threat lies in its unseen and unsanctioned nature. Often deployed to fulfill immediate needs, these services don't go through the same stringent security controls and vetting process of traditional IT security, potentially bypassing existing security protocols and exposing data to breaches.
Is your organization actively tracking the challenges that may arise as a result of unapproved devices and services being used?
- Traditional security measures might fail to recognize and regulate the ephemeral and hidden nature of unsanctioned activities effectively. In such instances, unusual data transfers or the sudden appearance of unexpected nodes in the network may go unnoticed or, worse, misinterpreted as regular activity.
- The effort to constantly track down and monitor unsanctioned activities can result in resource strain, where you're continually on the hunt without knowing where the next problem will arise.
- Unapproved tools and services may not fit seamlessly into an organization's tech stack, leading to data silos that compromise data analysis and interpretation.
Best Practices of Implementing DSPM
Implementing DSPM requires meticulous planning, strategic vision, and ongoing commitment. The key lies in striking the optimum balance between automated and manual controls, between prevention and detection strategies, and between flexibility and rigidity of security protocols.
Centralized Security Management
The cornerstone of any robust DSPM strategy lies in its centralized management. This involves the aggregation, correlation, and analysis of security data from across the organization in one place. A centralized approach enhances visibility into security postures, reduces fragmentation of controls, and facilitates quicker response times. Incorporating advanced security orchestration, automation, and response (SOAR) tools can help in executing this strategy efficiently.
Continuous Monitoring
Ensuring a strong security posture is not a one-time task but a continuous commitment. Regular auditing, real-time monitoring, and proactive threat hunting are core aspects of staying ahead in the evolving threat landscape. Automated monitoring tools can assist in tracking deviations from the desired posture, while anomaly detection algorithms can help identify unusual activity or patterns.
Intelligent Alerting
The efficacy of DSPM implementation hinges largely on its capacity to generate actionable insights. Establishing an intelligent alerting system can help separate the signal from unwanted noise. This reduces the chances of alert fatigue, and ensures the prompt attention of relevant personnel to critical security threats. Consider incorporating machine learning algorithms to improve the alerting system's accuracy and efficiency over time.
Automated Remediation
Speed is of the essence when responding to security incidents. Automated remediation tools can offer instant reactions to known threats, thereby reducing the window of exposure. This automation not only allows for quicker response times but also reduces the manual workload, freeing up your security team to focus on more complex issues that demand human intervention.
Regular Training and Awareness Programs
The human element in cybersecurity cannot be understated. Regular training programs for employees can be instrumental in preventing avoidable security breaches. Such programs can help inculcate a culture of security, enhance understanding of security protocols, and reduce susceptibility to social engineering attacks.
What's the Difference Between CSPM and DSPM?
Cloud Security Posture Management (CSPM) solutions are built to secure cloud infrastructure while DSPM is focused on cloud data. The difference is significant. A CSPM is built to find vulnerabilities in cloud resources, like VMs and VPC networks. Some may also be able to provide very basic insights on the data, like identifying PII in text files in VMs and S3 buckets. Beyond these basic abilities, CSPM products are often data agnostic and don’t prioritize remediation based on data sensitivity.
DSPM, on the other hand, is about the data itself. This includes identifying data vulnerabilities like overexposure, access controls, data flows, and anomalies. A DPSM solution connects the dots between data and the infrastructure security, allowing security teams to understand what sensitive data is at risk instead of showing them a list of vulnerabilities to remediate. Essentially DSPM is adding a layer of data security and data context over the infrastructure security.
Besides adding a layer of data security and data context on top of Cloud Security Posture Management, DSPM offers several advantages:
Read more about DSPM vs. CSPM.
Exploring the Right DSPM Tools
DSPM tools have emerged as vital solutions in the modern hybrid computing landscape, where data flows between various cloud and on-premises environments, each with distinct security postures. Traditional security tools focused on static perimeters struggle to keep up with the dynamic nature of data movement. DSPM takes a data-centric approach to ensure sensitive data maintains the right security posture, offering automatic visibility, risk assessment, and access analysis for cloud data.
One significant challenge in protecting sensitive data is dealing with data clutter, including unused copies and outdated versions scattered across the organization. DSPM tools address this issue by continuously monitoring the states and versions of sensitive data, providing guidelines for remediation, and reducing the data attack surface. These tools offer agile threat intelligence, enhanced data governance, granular access controls, efficient threat response, and seamless integration with major cloud providers.
Organizations should consider DSPM tools when operating in multi-cloud environments, frequently replicating data, managing complex access control, or needing to comply with stringent data protection regulations.
Read more about DSPM tools.
Conclusion
Administering a robust cloud data security strategy isn't easy. You must know where your data is, who accesses it, and how its misuse can be defended. Understanding your cloud provider's security measures and the shared-responsibility model is also vital.
Data Security Posture Management is new, and with that comes the natural skepticism of ‘do we really need another security acronym?’. However, DSPM is solving real security problems caused by the move to the cloud and can help prevent major data breaches.
Customer information, company secrets, and source code leaks aren't caused by initial failures to protect sensitive data. They’re caused by the ease with which data is replicated and moved around - without the security posture following. Data Security Posture Management promises to make sure that wherever your data travels in the cloud - your security posture follows and data risks are minimized.
DSPM represents a critical pillar in supporting the data security mix – helping control data security for all users, devices and software, and providing complete visibility into data in use, data in motion and data at rest. Advanced DSPM solutions like Sentra ensure the security and privacy of sensitive data not just across cloud providers, but also within applications, containers, and workloads.
To learn more about how Sentra’s DSPM solution can help secure your cloud data, contact us or just go ahead and watch a demo today!
What is the difference between DLP and DSPM?
Distinguishing between Data Loss Prevention (DLP) and Data Security Posture Management (DSPM) elucidates their distinct contributions to safeguarding sensitive information.
DLP (Data Loss Prevention):
- Focuses on preventing unauthorized data exposure or leakage.
- Utilizes content analysis and policy enforcement to identify and mitigate potential data breaches.
- Monitors data in motion, at rest, and in use to prevent accidental or intentional data loss.
DSPM (Data Security Posture Management):
- Encompasses a comprehensive approach, covering the entire lifecycle of data.
- Involves managing data access, classification, encryption, and user behavior analysis.
- Aims to protect data holistically, including prevention of unauthorized access and misuse.
While DLP emphasizes preventing data leakage, DSPM takes a broader approach by ensuring comprehensive data protection throughout its lifecycle. Choosing between DLP and DSPM depends on the organization's security needs and objectives related to sensitive data.
What is a data security platform?
Exploring the concept of a Data Security Platform unveils its multifaceted role in fortifying data protection strategies. A Data Security Platform is a comprehensive solution that encompasses various features, including:
- Data Discovery: Identifying sensitive data across diverse systems and repositories.
- Data Classification: Categorizing data based on its sensitivity for targeted protection.
- Data Loss Prevention (DLP): Preventing unauthorized data exposure or leakage.
- Encryption: Implementing encryption techniques to safeguard data in transit and at rest.
- Access Control: Managing user permissions to ensure data is accessible only to authorized individuals.
- User Behavior Analytics: Monitoring user activities for detecting unusual patterns.
- Threat Detection: Identifying potential security breaches and anomalies in real-time.
- Compliance Management: Ensuring data security practices align with industry regulations.
A Data Security Platform offers a comprehensive approach to safeguarding data, integrating diverse functionalities that collectively enhance data protection, regulatory compliance, and overall cybersecurity posture.
What is a security posture?
A security posture represents an organization's comprehensive approach to cybersecurity, it encompasses all strategies and practices to defend against cyber threats effectively. Some of the important practices are:
- Risk Management: Identifying, assessing, and mitigating potential risks to assets and data.
- Security Policies: Establishing guidelines and protocols to govern security practices.
- Access Controls: Managing who can access resources and data, minimizing vulnerabilities.
- Threat Detection: Employing tools and practices to identify and respond to security threats.
- Incident Response: Preparing procedures to handle and recover from security incidents.
- Employee Training: Educating personnel on security best practices and potential risks.
- Compliance Measures: Ensuring adherence to industry regulations and standards.
- Regular Assessments: Periodically evaluating security measures for effectiveness.
A robust security posture integrates diverse elements to proactively safeguard an organization's digital assets, infrastructure, and sensitive information against a broad spectrum of cyber threats.
What is data security control for securing data in the cloud?
Embarking on an exploration of data security controls within cloud environments reveals the pivotal techniques that underpin the protection of sensitive information against the backdrop of an evolving digital landscape. Data security controls for cloud data protection encompass:
- Encryption: Implementing encryption techniques for data at rest and in transit.
- Access Control: Managing user permissions and restricting data access to authorized individuals.
- Authentication: Verifying user identities through multi-factor authentication methods.
- Auditing and Monitoring: Tracking data activities and changes to identify anomalies.
- Data Classification: Categorizing data based on sensitivity to apply appropriate security measures.
- Backup and Recovery: Regularly backing up data and establishing recovery plans.
- Data Loss Prevention (DLP): Employing mechanisms to prevent data leakage.
- Vulnerability Management: Continuously assessing and addressing vulnerabilities in the cloud environment.
Employing a combination of these data security controls forms a robust defense against potential threats, ensuring data integrity, confidentiality, and availability in cloud storage.
What are DSPM tools?
Understanding Data Security Posture Management (DSPM) tools is essential for comprehending their pivotal role in maintaining data security across its lifecycle.
DSPM tools encompass a range of functions, including:
- Data Discovery and Classification: This helps organizations identify and classify sensitive data, such as personally identifiable information (PII), financial data, and intellectual property.
- Data Protection: This helps organizations control how sensitive data is accessed, shared, and moved. This can be done through a variety of methods, such as encryption, access control, and monitoring.
- User Activity Monitoring: This helps organizations track user activity to identify suspicious behavior. This can help detect and prevent data breaches.
- Reporting and Analytics: This helps organizations generate reports and analytics to track data security trends and identify potential risks. This information can be used to improve data security posture.
- Risk Assessment: This helps organizations assess the risk of data breaches and identify areas where they need to improve their security posture.
- Compliance Reporting: This helps organizations generate reports to demonstrate compliance with regulations, such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA).
Popular DSPM Tools:
- Sentra is a data lifecycle security platform that discovers, classifies, and secures sensitive cloud data across your cloud environments.
- Varonis is another popular DSPM tool that helps organizations protect their sensitive data from unauthorized access, theft, and misuse.
- Wiz is a cloud data security posture management (DSPM) tool that helps organizations identify and fix security misconfigurations and vulnerabilities in their cloud environments.
- Securiti is a cloud-native DSPM tool that provides visibility and control over data security across all environments, including cloud, on-premises, and SaaS.
DSPM tools are pivotal for robust data security management, encompassing a variety of functionalities that collectively safeguard data integrity and confidentiality throughout its lifecycle.
